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Our client is one of Europe’s largest Oil & Gas producers. We partnered with a leading AI & Cognitive computing company to deploy AI solutions in the client’s upstream production hubs.The objective was to enhance operational efficiency and reduce equipment downtime through intelligent automation.
Recurring failures in production subcomponents
Globally, Oil & Gas producing companies are looking to maximize their production capacity, increase efficiency, and improve safety by deploying AI-powered predictive analytics. On the other hand, machine failures lead to millions of dollars in losses for these large industrial companies. A report suggests that large plants lose approximately 323 production hours a year. For our client, recurring failures in production subcomponents resulted in >10% downtime and millions of dollars lost in production.
The same report estimates that the cost of revenue lost, penalties, idle resource time, and the cost of restarting lines can amount to $172 million per plant in a year.
ML based solution of the client utilized data from multiple offshore plant components to predict impending failures. Our team trained multiple advanced ML Models to predict output from different subcomponents like Compressors, Pumps, Gas dehydration, Oil treaters, degassers, separators, valves, etc. Our team also collected sensor data from the upstream system to train ML models.
Our data scientists started by making sense of thousands of tags, including pressure, vibration, temperature, and others. The team reduced the number of tags to about 130 important ones per subsystem. These tags needed further optimization via dimensionality reduction techniques to remove noise in the data.
This high-quality dataset can help build unsupervised models for each subcomponent. It can also identify new (previously unknown) operating states of sub-components.
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The tech stack used for this project included: Python, Django, TensorFlow, PyTorch, React, Rest API, AWS Lambda, & AWS RDS.
-With our ML models & advanced analytics, the client successfully identified 75% of production-impacting events with an average of 8 days in advance.
-The solution helped SMEs predict and diagnose impending failures and prioritize work orders accordingly.
-With reduced production downtime, annual production of the plants increased by $30M per plant.
-With predictive analytics capability, the client could take action on maintenance, reduce deferred production to 2 days with savings worth $10M
